code: def test_clipped_ppo_loss_continuous_batch():
      # check lower loss for policy with better probabilities:
      # i.e. higher probability on high advantage actions, low probability on low advantage actions.
      loss_fn = ClippedPPOLossContinuous(
          num_actions=2, clip_likelihood_ratio_using_epsilon=0.2
      )
      loss_fn.initialize()
      # actual actions taken, of shape (batch_size)
      actions = mx.nd.array(((0.5, -0.5), (0.2, 0.3), (0.4, 2.0)))
      # advantages from taking action, of shape (batch_size)
      advantages = mx.nd.array((2, -2, 1))
      # action probabilities, of shape (batch_size, num_actions)
      old_policy_means = mx.nd.array(((1, 0), (0, 0), (-1, 0)))
      new_policy_means_worse = mx.nd.array(((2, 0), (0, 0), (-1, 0)))
      new_policy_means_better = mx.nd.array(((0.5, 0), (0, 0), (-1, 0)))
  
      policy_stds = mx.nd.array(((1, 1), (1, 1), (1, 1)))
      clip_param_rescaler = mx.nd.array((1,))
  
      loss_worse = loss_fn(
          new_policy_means_worse,
          policy_stds,
          actions,
          old_policy_means,
          policy_stds,
          clip_param_rescaler,
          advantages,
      )
      loss_better = loss_fn(
          new_policy_means_better,
          policy_stds,
          actions,
          old_policy_means,
          policy_stds,
          clip_param_rescaler,
          advantages,
      )
  
      assert (
          len(loss_worse) == 6
      )  # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO)
      loss_worse_val = loss_worse[0]
      assert loss_worse_val.ndim == 1
      assert loss_worse_val.shape[0] == 1
      assert (
          len(loss_better) == 6
      )  # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO)
      loss_better_val = loss_better[0]
      assert loss_better_val.ndim == 1
      assert loss_better_val.shape[0] == 1
      assert loss_worse_val > loss_better_val
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 12 for calling a global function with example usage: test_clipped_ppo_loss_continuous_batch() without return types